文章目录

机器学习 | Kaggle鸢尾花数据集Iris训练一、准备工作：引入机器学习库二、数据可视化分析三、逻辑回归分类结果3.1 选取花萼特征分类3.2 选取花瓣特征分类 四、数据集切割（训练集+测试集）五、模型训练并用验证集验证5.1 逻辑回归5.2 决策树5.3 K-邻近5.4 支持向量机 六、神经网络七、将花瓣和花萼特征分离+标签 训练7.1 选取花萼特征训练7.1 选取花瓣特征训练

机器学习 | Kaggle鸢尾花数据集Iris训练

​ Wenxuan Zeng 2020.10.3

一、准备工作：引入机器学习库

# 引入机器学习库 from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import LabelBinarizer from sklearn import svm from sklearn import model_selection from sklearn import metrics import matplotlib.pyplot as plt import seaborn as sns import pandas as pd import numpy as np # 忽略不必要的报错 import warnings warnings.filterwarnings("ignore")

二、数据可视化分析

该题数据量很小（150组）特征值不多（4类）标签简单（3种），且题目要求在于分类，所以在做数据可视化分析的时候，不需要像考虑Titanic/Crime_prediction那样深度挖掘不同特征之间微妙的联系，我们直接分情况将散点图绘制在同一坐标系下即可观察。 # 载入sklearn库中的iris数据集 from sklearn.datasets import load_iris iris = load_iris() print (iris.data) [[5.1 3.5 1.4 0.2] [4.9 3. 1.4 0.2] [4.7 3.2 1.3 0.2] [4.6 3.1 1.5 0.2] [5. 3.6 1.4 0.2] [5.4 3.9 1.7 0.4] [4.6 3.4 1.4 0.3] [5. 3.4 1.5 0.2] [4.4 2.9 1.4 0.2] [4.9 3.1 1.5 0.1] [5.4 3.7 1.5 0.2] [4.8 3.4 1.6 0.2] [4.8 3. 1.4 0.1] [4.3 3. 1.1 0.1] [5.8 4. 1.2 0.2] [5.7 4.4 1.5 0.4] [5.4 3.9 1.3 0.4] [5.1 3.5 1.4 0.3] [5.7 3.8 1.7 0.3] [5.1 3.8 1.5 0.3] [5.4 3.4 1.7 0.2] [5.1 3.7 1.5 0.4] [4.6 3.6 1. 0.2] [5.1 3.3 1.7 0.5] [4.8 3.4 1.9 0.2] [5. 3. 1.6 0.2] [5. 3.4 1.6 0.4] [5.2 3.5 1.5 0.2] [5.2 3.4 1.4 0.2] [4.7 3.2 1.6 0.2] [4.8 3.1 1.6 0.2] [5.4 3.4 1.5 0.4] [5.2 4.1 1.5 0.1] [5.5 4.2 1.4 0.2] [4.9 3.1 1.5 0.2] [5. 3.2 1.2 0.2] [5.5 3.5 1.3 0.2] [4.9 3.6 1.4 0.1] [4.4 3. 1.3 0.2] [5.1 3.4 1.5 0.2] [5. 3.5 1.3 0.3] [4.5 2.3 1.3 0.3] [4.4 3.2 1.3 0.2] [5. 3.5 1.6 0.6] [5.1 3.8 1.9 0.4] [4.8 3. 1.4 0.3] [5.1 3.8 1.6 0.2] [4.6 3.2 1.4 0.2] [5.3 3.7 1.5 0.2] [5. 3.3 1.4 0.2] [7. 3.2 4.7 1.4] [6.4 3.2 4.5 1.5] [6.9 3.1 4.9 1.5] [5.5 2.3 4. 1.3] [6.5 2.8 4.6 1.5] [5.7 2.8 4.5 1.3] [6.3 3.3 4.7 1.6] [4.9 2.4 3.3 1. ] [6.6 2.9 4.6 1.3] [5.2 2.7 3.9 1.4] [5. 2. 3.5 1. ] [5.9 3. 4.2 1.5] [6. 2.2 4. 1. ] [6.1 2.9 4.7 1.4] [5.6 2.9 3.6 1.3] [6.7 3.1 4.4 1.4] [5.6 3. 4.5 1.5] [5.8 2.7 4.1 1. ] [6.2 2.2 4.5 1.5] [5.6 2.5 3.9 1.1] [5.9 3.2 4.8 1.8] [6.1 2.8 4. 1.3] [6.3 2.5 4.9 1.5] [6.1 2.8 4.7 1.2] [6.4 2.9 4.3 1.3] [6.6 3. 4.4 1.4] [6.8 2.8 4.8 1.4] [6.7 3. 5. 1.7] [6. 2.9 4.5 1.5] [5.7 2.6 3.5 1. ] [5.5 2.4 3.8 1.1] [5.5 2.4 3.7 1. ] [5.8 2.7 3.9 1.2] [6. 2.7 5.1 1.6] [5.4 3. 4.5 1.5] [6. 3.4 4.5 1.6] [6.7 3.1 4.7 1.5] [6.3 2.3 4.4 1.3] [5.6 3. 4.1 1.3] [5.5 2.5 4. 1.3] [5.5 2.6 4.4 1.2] [6.1 3. 4.6 1.4] [5.8 2.6 4. 1.2] [5. 2.3 3.3 1. ] [5.6 2.7 4.2 1.3] [5.7 3. 4.2 1.2] [5.7 2.9 4.2 1.3] [6.2 2.9 4.3 1.3] [5.1 2.5 3. 1.1] [5.7 2.8 4.1 1.3] [6.3 3.3 6. 2.5] [5.8 2.7 5.1 1.9] [7.1 3. 5.9 2.1] [6.3 2.9 5.6 1.8] [6.5 3. 5.8 2.2] [7.6 3. 6.6 2.1] [4.9 2.5 4.5 1.7] [7.3 2.9 6.3 1.8] [6.7 2.5 5.8 1.8] [7.2 3.6 6.1 2.5] [6.5 3.2 5.1 2. ] [6.4 2.7 5.3 1.9] [6.8 3. 5.5 2.1] [5.7 2.5 5. 2. ] [5.8 2.8 5.1 2.4] [6.4 3.2 5.3 2.3] [6.5 3. 5.5 1.8] [7.7 3.8 6.7 2.2] [7.7 2.6 6.9 2.3] [6. 2.2 5. 1.5] [6.9 3.2 5.7 2.3] [5.6 2.8 4.9 2. ] [7.7 2.8 6.7 2. ] [6.3 2.7 4.9 1.8] [6.7 3.3 5.7 2.1] [7.2 3.2 6. 1.8] [6.2 2.8 4.8 1.8] [6.1 3. 4.9 1.8] [6.4 2.8 5.6 2.1] [7.2 3. 5.8 1.6] [7.4 2.8 6.1 1.9] [7.9 3.8 6.4 2. ] [6.4 2.8 5.6 2.2] [6.3 2.8 5.1 1.5] [6.1 2.6 5.6 1.4] [7.7 3. 6.1 2.3] [6.3 3.4 5.6 2.4] [6.4 3.1 5.5 1.8] [6. 3. 4.8 1.8] [6.9 3.1 5.4 2.1] [6.7 3.1 5.6 2.4] [6.9 3.1 5.1 2.3] [5.8 2.7 5.1 1.9] [6.8 3.2 5.9 2.3] [6.7 3.3 5.7 2.5] [6.7 3. 5.2 2.3] [6.3 2.5 5. 1.9] [6.5 3. 5.2 2. ] [6.2 3.4 5.4 2.3] [5.9 3. 5.1 1.8]] # iris的三个种类 各有50个 共150个 print (iris.target) print (len(iris.target)) # 共150个数据 每个iris有4个特征属性 print (iris.data.shape) [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2] 150 (150, 4) iris中包含iris.data和iris.target，其中iris.data是150x4的矩阵，存放四特征（花瓣和花萼长宽值），iris.target是150x1的矩阵，存放150朵花的标签（类别：0，1，2）。这三种类别的花分别50朵，分布在数据集的前50，中50，后50. # 获取花卉一二列特征数据集（花萼特征） DD = iris.data X = [x[0] for x in DD] print (X) Y = [x[1] for x in DD] print (Y) #plt.scatter(X, Y, c=iris.target, marker='x') # 第一类 前50个样本 plt.scatter(X[:50], Y[:50], color='red', marker='o', label='setosa') # 第二类 中间50个样本 plt.scatter(X[50:100], Y[50:100], color='blue', marker='x', label='versicolor') # 第三类 后50个样本 plt.scatter(X[100:], Y[100:],color='green', marker='+', label='Virginica') # 图例 plt.legend(loc=2) #左上角 plt.show() [5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9] [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3.0, 3.0, 4.0, 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3.0, 3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3.0, 3.4, 3.5, 2.3, 3.2, 3.5, 3.8, 3.0, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2.0, 3.0, 2.2, 2.9, 2.9, 3.1, 3.0, 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3.0, 2.8, 3.0, 2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3.0, 3.4, 3.1, 2.3, 3.0, 2.5, 2.6, 3.0, 2.6, 2.3, 2.7, 3.0, 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3.0, 2.9, 3.0, 3.0, 2.5, 2.9, 2.5, 3.6, 3.2, 2.7, 3.0, 2.5, 2.8, 3.2, 3.0, 3.8, 2.6, 2.2, 3.2, 2.8, 2.8, 2.7, 3.3, 3.2, 2.8, 3.0, 2.8, 3.0, 2.8, 3.8, 2.8, 2.8, 2.6, 3.0, 3.4, 3.1, 3.0, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3.0, 2.5, 3.0, 3.4, 3.0]

该组出现的问题即是：选取花萼宽度和长度特征并不能有效将蓝色和绿色离散点分开，所以相关度较低。 # 获取花卉三四列特征数据集 （花瓣特征） DD = iris.data X = [x[2] for x in DD] print (X) Y = [x[3] for x in DD] print (Y) #plt.scatter(X, Y, c=iris.target, marker='x') # 第一类 前50个样本 plt.scatter(X[:50], Y[:50], color='red', marker='o', label='setosa') # 第二类 中间50个样本 plt.scatter(X[50:100], Y[50:100], color='blue', marker='x', label='versicolor') # 第三类 后50个样本 plt.scatter(X[100:], Y[100:],color='green', marker='+', label='Virginica') # 图例 plt.legend(loc=2) #左上角 plt.show() [1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4.0, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4.0, 4.9, 4.7, 4.3, 4.4, 4.8, 5.0, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4.0, 4.4, 4.6, 4.0, 3.3, 4.2, 4.2, 4.2, 4.3, 3.0, 4.1, 6.0, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5.0, 5.1, 5.3, 5.5, 6.7, 6.9, 5.0, 5.7, 4.9, 6.7, 4.9, 5.7, 6.0, 4.8, 4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5.0, 5.2, 5.4, 5.1] [0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4, 1.0, 1.5, 1.0, 1.4, 1.3, 1.4, 1.5, 1.0, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1.0, 1.1, 1.0, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2.0, 1.9, 2.1, 2.0, 2.4, 2.3, 1.8, 2.2, 2.3, 1.5, 2.3, 2.0, 2.0, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2.0, 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 1.9, 2.0, 2.3, 1.8]

# 获取花卉一四列特征数据集 DD = iris.data X = [x[0] for x in DD] print (X) Y = [x[3] for x in DD] print (Y) #plt.scatter(X, Y, c=iris.target, marker='x') # 第一类 前50个样本 plt.scatter(X[:50], Y[:50], color='red', marker='o', label='setosa') # 第二类 中间50个样本 plt.scatter(X[50:100], Y[50:100], color='blue', marker='x', label='versicolor') # 第三类 后50个样本 plt.scatter(X[100:], Y[100:],color='green', marker='+', label='Virginica') # 图例 plt.legend(loc=2) #左上角 plt.show() [5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9] [0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4, 1.0, 1.5, 1.0, 1.4, 1.3, 1.4, 1.5, 1.0, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1.0, 1.1, 1.0, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2.0, 1.9, 2.1, 2.0, 2.4, 2.3, 1.8, 2.2, 2.3, 1.5, 2.3, 2.0, 2.0, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2.0, 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 1.9, 2.0, 2.3, 1.8]

# 获取花卉一三列特征数据集 DD = iris.data X = [x[0] for x in DD] print (X) Y = [x[2] for x in DD] print (Y) #plt.scatter(X, Y, c=iris.target, marker='x') # 第一类 前50个样本 plt.scatter(X[:50], Y[:50], color='red', marker='o', label='setosa') # 第二类 中间50个样本 plt.scatter(X[50:100], Y[50:100], color='blue', marker='x', label='versicolor') # 第三类 后50个样本 plt.scatter(X[100:], Y[100:],color='green', marker='+', label='Virginica') # 图例 plt.legend(loc=2) #左上角 plt.show() [5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9] [1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4.0, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4.0, 4.9, 4.7, 4.3, 4.4, 4.8, 5.0, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4.0, 4.4, 4.6, 4.0, 3.3, 4.2, 4.2, 4.2, 4.3, 3.0, 4.1, 6.0, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5.0, 5.1, 5.3, 5.5, 6.7, 6.9, 5.0, 5.7, 4.9, 6.7, 4.9, 5.7, 6.0, 4.8, 4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5.0, 5.2, 5.4, 5.1]

# 获取花卉二三列特征数据集 DD = iris.data X = [x[1] for x in DD] print (X) Y = [x[2] for x in DD] print (Y) #plt.scatter(X, Y, c=iris.target, marker='x') # 第一类 前50个样本 plt.scatter(X[:50], Y[:50], color='red', marker='o', label='setosa') # 第二类 中间50个样本 plt.scatter(X[50:100], Y[50:100], color='blue', marker='x', label='versicolor') # 第三类 后50个样本 plt.scatter(X[100:], Y[100:],color='green', marker='+', label='Virginica') # 图例 plt.legend(loc=2) #左上角 plt.show() [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3.0, 3.0, 4.0, 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3.0, 3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3.0, 3.4, 3.5, 2.3, 3.2, 3.5, 3.8, 3.0, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2.0, 3.0, 2.2, 2.9, 2.9, 3.1, 3.0, 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3.0, 2.8, 3.0, 2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3.0, 3.4, 3.1, 2.3, 3.0, 2.5, 2.6, 3.0, 2.6, 2.3, 2.7, 3.0, 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3.0, 2.9, 3.0, 3.0, 2.5, 2.9, 2.5, 3.6, 3.2, 2.7, 3.0, 2.5, 2.8, 3.2, 3.0, 3.8, 2.6, 2.2, 3.2, 2.8, 2.8, 2.7, 3.3, 3.2, 2.8, 3.0, 2.8, 3.0, 2.8, 3.8, 2.8, 2.8, 2.6, 3.0, 3.4, 3.1, 3.0, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3.0, 2.5, 3.0, 3.4, 3.0] [1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4.0, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4.0, 4.9, 4.7, 4.3, 4.4, 4.8, 5.0, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4.0, 4.4, 4.6, 4.0, 3.3, 4.2, 4.2, 4.2, 4.3, 3.0, 4.1, 6.0, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5.0, 5.1, 5.3, 5.5, 6.7, 6.9, 5.0, 5.7, 4.9, 6.7, 4.9, 5.7, 6.0, 4.8, 4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5.0, 5.2, 5.4, 5.1]

# 获取花卉二四列特征数据集 DD = iris.data X = [x[1] for x in DD] print (X) Y = [x[3] for x in DD] print (Y) #plt.scatter(X, Y, c=iris.target, marker='x') # 第一类 前50个样本 plt.scatter(X[:50], Y[:50], color='red', marker='o', label='setosa') # 第二类 中间50个样本 plt.scatter(X[50:100], Y[50:100], color='blue', marker='x', label='versicolor') # 第三类 后50个样本 plt.scatter(X[100:], Y[100:],color='green', marker='+', label='Virginica') # 图例 plt.legend(loc=2) #左上角 plt.show() [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3.0, 3.0, 4.0, 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3.0, 3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3.0, 3.4, 3.5, 2.3, 3.2, 3.5, 3.8, 3.0, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2.0, 3.0, 2.2, 2.9, 2.9, 3.1, 3.0, 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3.0, 2.8, 3.0, 2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3.0, 3.4, 3.1, 2.3, 3.0, 2.5, 2.6, 3.0, 2.6, 2.3, 2.7, 3.0, 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3.0, 2.9, 3.0, 3.0, 2.5, 2.9, 2.5, 3.6, 3.2, 2.7, 3.0, 2.5, 2.8, 3.2, 3.0, 3.8, 2.6, 2.2, 3.2, 2.8, 2.8, 2.7, 3.3, 3.2, 2.8, 3.0, 2.8, 3.0, 2.8, 3.8, 2.8, 2.8, 2.6, 3.0, 3.4, 3.1, 3.0, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3.0, 2.5, 3.0, 3.4, 3.0] [0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4, 1.0, 1.5, 1.0, 1.4, 1.3, 1.4, 1.5, 1.0, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1.0, 1.1, 1.0, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2.0, 1.9, 2.1, 2.0, 2.4, 2.3, 1.8, 2.2, 2.3, 1.5, 2.3, 2.0, 2.0, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2.0, 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 1.9, 2.0, 2.3, 1.8]

三、逻辑回归分类结果

3.1 选取花萼特征分类

# 获取花卉前两列数据集 X = X = iris.data[:, 0:2] Y = iris.target # 逻辑回归模型 lr = LogisticRegression(C=1e5) lr.fit(X,Y) # meshgrid函数生成两个网格矩阵 h = .02 x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5 y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # pcolormesh函数将xx,yy两个网格矩阵和对应的预测结果Z绘制在图片上 Z = lr.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) plt.figure(1, figsize=(8,6)) plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired) # 绘制散点图 plt.scatter(X[:50,0], X[:50,1], color='red',marker='o', label='setosa') plt.scatter(X[50:100,0], X[50:100,1], color='blue', marker='x', label='versicolor') plt.scatter(X[100:,0], X[100:,1], color='green', marker='s', label='Virginica') plt.xlabel('Sepal length') plt.ylabel('Sepal width') plt.xlim(xx.min(), xx.max()) plt.ylim(yy.min(), yy.max()) plt.xticks(()) plt.yticks(()) plt.legend(loc=2) plt.show()

3.2 选取花瓣特征分类

# 获取花卉后两列数据集 X = X = iris.data[:, 2:4] Y = iris.target # 逻辑回归模型 lr = LogisticRegression(C=1e5) lr.fit(X,Y) # meshgrid函数生成两个网格矩阵 h = .02 x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5 y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # pcolormesh函数将xx,yy两个网格矩阵和对应的预测结果Z绘制在图片上 Z = lr.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) plt.figure(1, figsize=(8,6)) plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired) # 绘制散点图 plt.scatter(X[:50,0], X[:50,1], color='red',marker='o', label='setosa') plt.scatter(X[50:100,0], X[50:100,1], color='blue', marker='x', label='versicolor') plt.scatter(X[100:,0], X[100:,1], color='green', marker='s', label='Virginica') plt.xlabel('Sepal length') plt.ylabel('Sepal width') plt.xlim(xx.min(), xx.max()) plt.ylim(yy.min(), yy.max()) plt.xticks(()) plt.yticks(()) plt.legend(loc=2) plt.show()

四、数据集切割（训练集+测试集）

x=iris.data y=iris.target x_train,x_test,y_train,y_test=model_selection.train_test_split(x,y,random_state=101,test_size=0.3) print("split_train_data 70%:", x_train.shape, "split_train_target 70%:",y_train.shape, "split_test_data 30%", x_test.shape, "split_test_target 30%",y_test.shape) split_train_data 70%: (105, 4) split_train_target 70%: (105,) split_test_data 30% (45, 4) split_test_target 30% (45,)

五、模型训练并用验证集验证

5.1 逻辑回归

# Logistic Regression model = LogisticRegression() model.fit(x_train, y_train) prediction=model.predict(x_test) print('The accuracy of the Logistic Regression is: {0}'.format(metrics.accuracy_score(prediction,y_test))) The accuracy of the Logistic Regression is: 0.9555555555555556

5.2 决策树

# DecisionTreeClassifier model=DecisionTreeClassifier() model.fit(x_train, y_train) prediction=model.predict(x_test) print('The accuracy of the DecisionTreeClassifier is: {0}'.format(metrics.accuracy_score(prediction,y_test))) The accuracy of the DecisionTreeClassifier is: 0.9555555555555556

5.3 K-邻近

# K-Nearest Neighbours model=KNeighborsClassifier(n_neighbors=3) model.fit(x_train, y_train) prediction=model.predict(x_test) print('The accuracy of the K-Nearest Neighbours is: {0}'.format(metrics.accuracy_score(prediction,y_test))) The accuracy of the K-Nearest Neighbours is: 1.0

5.4 支持向量机

# Support Vector Machine model = svm.SVC() model.fit(x_train, y_train) prediction=model.predict(x_test) print('The accuracy of the SVM is: {0}'.format(metrics.accuracy_score(prediction,y_test))) The accuracy of the SVM is: 1.0

六、神经网络

x=iris.data y=iris.target np.random.seed(seed=7) y_Label=LabelBinarizer().fit_transform(y) x_train,y_train,x_test,y_test=model_selection.train_test_split(x,y_Label,test_size=0.3,random_state=42) from keras.models import Sequential from keras.layers.core import Dense model = Sequential() #建立模型 model.add(Dense(4,activation='relu',input_shape=(4,))) model.add(Dense(6,activation='relu')) model.add(Dense(3,activation='softmax')) model.summary() Model: "sequential_23" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_68 (Dense) (None, 4) 20 _________________________________________________________________ dense_69 (Dense) (None, 6) 30 _________________________________________________________________ dense_70 (Dense) (None, 3) 21 ================================================================= Total params: 71 Trainable params: 71 Non-trainable params: 0 _________________________________________________________________ model.compile(loss='categorical_crossentropy',optimizer='rmsprop',metrics=['accuracy']) step=25 history=model.fit(x_train,x_test,validation_data=(y_train,y_test),batch_size=10,epochs=step) train_result=history.history Train on 105 samples, validate on 45 samples Epoch 1/25 105/105 [==============================] - 0s 2ms/step - loss: 0.1160 - accuracy: 0.9429 - val_loss: 0.0453 - val_accuracy: 1.0000 Epoch 2/25 105/105 [==============================] - 0s 123us/step - loss: 0.1121 - accuracy: 0.9524 - val_loss: 0.0486 - val_accuracy: 1.0000 Epoch 3/25 105/105 [==============================] - 0s 114us/step - loss: 0.1116 - accuracy: 0.9429 - val_loss: 0.0496 - val_accuracy: 1.0000 Epoch 4/25 105/105 [==============================] - 0s 114us/step - loss: 0.1129 - accuracy: 0.9524 - val_loss: 0.0479 - val_accuracy: 1.0000 Epoch 5/25 105/105 [==============================] - 0s 123us/step - loss: 0.1136 - accuracy: 0.9524 - val_loss: 0.0483 - val_accuracy: 1.0000 Epoch 6/25 105/105 [==============================] - 0s 114us/step - loss: 0.1112 - accuracy: 0.9524 - val_loss: 0.0518 - val_accuracy: 1.0000 Epoch 7/25 105/105 [==============================] - 0s 114us/step - loss: 0.1110 - accuracy: 0.9429 - val_loss: 0.0505 - val_accuracy: 1.0000 Epoch 8/25 105/105 [==============================] - 0s 123us/step - loss: 0.1099 - accuracy: 0.9524 - val_loss: 0.0467 - val_accuracy: 1.0000 Epoch 9/25 105/105 [==============================] - 0s 114us/step - loss: 0.1109 - accuracy: 0.9524 - val_loss: 0.0554 - val_accuracy: 0.9778 Epoch 10/25 105/105 [==============================] - 0s 123us/step - loss: 0.1097 - accuracy: 0.9524 - val_loss: 0.0535 - val_accuracy: 1.0000 Epoch 11/25 105/105 [==============================] - 0s 114us/step - loss: 0.1091 - accuracy: 0.9524 - val_loss: 0.0452 - val_accuracy: 1.0000 Epoch 12/25 105/105 [==============================] - 0s 123us/step - loss: 0.1040 - accuracy: 0.9619 - val_loss: 0.0742 - val_accuracy: 0.9778 Epoch 13/25 105/105 [==============================] - 0s 123us/step - loss: 0.1124 - accuracy: 0.9524 - val_loss: 0.0670 - val_accuracy: 0.9778 Epoch 14/25 105/105 [==============================] - ETA: 0s - loss: 0.0918 - accuracy: 0.90 - 0s 133us/step - loss: 0.1110 - accuracy: 0.9429 - val_loss: 0.0746 - val_accuracy: 0.9778 Epoch 15/25 105/105 [==============================] - 0s 114us/step - loss: 0.1105 - accuracy: 0.9429 - val_loss: 0.0545 - val_accuracy: 0.9778 Epoch 16/25 105/105 [==============================] - 0s 123us/step - loss: 0.1077 - accuracy: 0.9524 - val_loss: 0.0519 - val_accuracy: 1.0000 Epoch 17/25 105/105 [==============================] - 0s 114us/step - loss: 0.1113 - accuracy: 0.9524 - val_loss: 0.0562 - val_accuracy: 0.9778 Epoch 18/25 105/105 [==============================] - 0s 123us/step - loss: 0.1085 - accuracy: 0.9524 - val_loss: 0.0491 - val_accuracy: 1.0000 Epoch 19/25 105/105 [==============================] - 0s 123us/step - loss: 0.1101 - accuracy: 0.9524 - val_loss: 0.0548 - val_accuracy: 0.9778 Epoch 20/25 105/105 [==============================] - 0s 114us/step - loss: 0.1060 - accuracy: 0.9429 - val_loss: 0.0451 - val_accuracy: 1.0000 Epoch 21/25 105/105 [==============================] - 0s 123us/step - loss: 0.1128 - accuracy: 0.9524 - val_loss: 0.0450 - val_accuracy: 1.0000 Epoch 22/25 105/105 [==============================] - 0s 123us/step - loss: 0.1086 - accuracy: 0.9524 - val_loss: 0.0534 - val_accuracy: 0.9778 Epoch 23/25 105/105 [==============================] - 0s 123us/step - loss: 0.1065 - accuracy: 0.9524 - val_loss: 0.0455 - val_accuracy: 1.0000 Epoch 24/25 105/105 [==============================] - 0s 114us/step - loss: 0.1062 - accuracy: 0.9524 - val_loss: 0.0434 - val_accuracy: 1.0000 Epoch 25/25 105/105 [==============================] - 0s 123us/step - loss: 0.1081 - accuracy: 0.9524 - val_loss: 0.0423 - val_accuracy: 1.0000 基于keras的全连接神经网络模型，仅仅需要很少的训练次数，即可达到100%的准确度，是训练iris数据集时不错的选择。 # 从最终的训练模型种读取准确度 acc=train_result['accuracy'] val_acc=train_result['val_accuracy'] epochs=range(1,step+1) plt.plot(epochs,acc,'b-') plt.plot(epochs,val_acc,'r') plt.xlabel('epochs') plt.ylabel('accuracy') plt.show() t=model.predict(y_train) resultsss=model.evaluate(y_train,y_test) resultsss

45/45 [==============================] - 0s 44us/step [0.5691331187884013, 0.9111111164093018]

七、将花瓣和花萼特征分离+标签 训练

7.1 选取花萼特征训练

# 获取花卉一二列特征数据集 DD = iris.data x=DD[ :,0:2] print(x) y=iris.target [[5.1 3.5] [4.9 3. ] [4.7 3.2] [4.6 3.1] [5. 3.6] [5.4 3.9] [4.6 3.4] [5. 3.4] [4.4 2.9] [4.9 3.1] [5.4 3.7] [4.8 3.4] [4.8 3. ] [4.3 3. ] [5.8 4. ] [5.7 4.4] [5.4 3.9] [5.1 3.5] [5.7 3.8] [5.1 3.8] [5.4 3.4] [5.1 3.7] [4.6 3.6] [5.1 3.3] [4.8 3.4] [5. 3. ] [5. 3.4] [5.2 3.5] [5.2 3.4] [4.7 3.2] [4.8 3.1] [5.4 3.4] [5.2 4.1] [5.5 4.2] [4.9 3.1] [5. 3.2] [5.5 3.5] [4.9 3.6] [4.4 3. ] [5.1 3.4] [5. 3.5] [4.5 2.3] [4.4 3.2] [5. 3.5] [5.1 3.8] [4.8 3. ] [5.1 3.8] [4.6 3.2] [5.3 3.7] [5. 3.3] [7. 3.2] [6.4 3.2] [6.9 3.1] [5.5 2.3] [6.5 2.8] [5.7 2.8] [6.3 3.3] [4.9 2.4] [6.6 2.9] [5.2 2.7] [5. 2. ] [5.9 3. ] [6. 2.2] [6.1 2.9] [5.6 2.9] [6.7 3.1] [5.6 3. ] [5.8 2.7] [6.2 2.2] [5.6 2.5] [5.9 3.2] [6.1 2.8] [6.3 2.5] [6.1 2.8] [6.4 2.9] [6.6 3. ] [6.8 2.8] [6.7 3. ] [6. 2.9] [5.7 2.6] [5.5 2.4] [5.5 2.4] [5.8 2.7] [6. 2.7] [5.4 3. ] [6. 3.4] [6.7 3.1] [6.3 2.3] [5.6 3. ] [5.5 2.5] [5.5 2.6] [6.1 3. ] [5.8 2.6] [5. 2.3] [5.6 2.7] [5.7 3. ] [5.7 2.9] [6.2 2.9] [5.1 2.5] [5.7 2.8] [6.3 3.3] [5.8 2.7] [7.1 3. ] [6.3 2.9] [6.5 3. ] [7.6 3. ] [4.9 2.5] [7.3 2.9] [6.7 2.5] [7.2 3.6] [6.5 3.2] [6.4 2.7] [6.8 3. ] [5.7 2.5] [5.8 2.8] [6.4 3.2] [6.5 3. ] [7.7 3.8] [7.7 2.6] [6. 2.2] [6.9 3.2] [5.6 2.8] [7.7 2.8] [6.3 2.7] [6.7 3.3] [7.2 3.2] [6.2 2.8] [6.1 3. ] [6.4 2.8] [7.2 3. ] [7.4 2.8] [7.9 3.8] [6.4 2.8] [6.3 2.8] [6.1 2.6] [7.7 3. ] [6.3 3.4] [6.4 3.1] [6. 3. ] [6.9 3.1] [6.7 3.1] [6.9 3.1] [5.8 2.7] [6.8 3.2] [6.7 3.3] [6.7 3. ] [6.3 2.5] [6.5 3. ] [6.2 3.4] [5.9 3. ]] x_train,x_test,y_train,y_test=model_selection.train_test_split(x,y,random_state=101,test_size=0.3) print("split_train_data 70%:", x_train.shape, "split_train_target 70%:",y_train.shape, "split_test_data 30%", x_test.shape, "split_test_target 30%",y_test.shape) split_train_data 70%: (105, 2) split_train_target 70%: (105,) split_test_data 30% (45, 2) split_test_target 30% (45,) # K-Nearest Neighbours model=KNeighborsClassifier(n_neighbors=3) model.fit(x_train, y_train) prediction=model.predict(x_test) print('The accuracy of the K-Nearest Neighbours is: {0}'.format(metrics.accuracy_score(prediction,y_test))) The accuracy of the K-Nearest Neighbours is: 0.6444444444444445

7.1 选取花瓣特征训练

DD = iris.data x=DD[ :,2:4] print(x) [[1.4 0.2] [1.4 0.2] [1.3 0.2] [1.5 0.2] [1.4 0.2] [1.7 0.4] [1.4 0.3] [1.5 0.2] [1.4 0.2] [1.5 0.1] [1.5 0.2] [1.6 0.2] [1.4 0.1] [1.1 0.1] [1.2 0.2] [1.5 0.4] [1.3 0.4] [1.4 0.3] [1.7 0.3] [1.5 0.3] [1.7 0.2] [1.5 0.4] [1. 0.2] [1.7 0.5] [1.9 0.2] [1.6 0.2] [1.6 0.4] [1.5 0.2] [1.4 0.2] [1.6 0.2] [1.6 0.2] [1.5 0.4] [1.5 0.1] [1.4 0.2] [1.5 0.2] [1.2 0.2] [1.3 0.2] [1.4 0.1] [1.3 0.2] [1.5 0.2] [1.3 0.3] [1.3 0.3] [1.3 0.2] [1.6 0.6] [1.9 0.4] [1.4 0.3] [1.6 0.2] [1.4 0.2] [1.5 0.2] [1.4 0.2] [4.7 1.4] [4.5 1.5] [4.9 1.5] [4. 1.3] [4.6 1.5] [4.5 1.3] [4.7 1.6] [3.3 1. ] [4.6 1.3] [3.9 1.4] [3.5 1. ] [4.2 1.5] [4. 1. ] [4.7 1.4] [3.6 1.3] [4.4 1.4] [4.5 1.5] [4.1 1. ] [4.5 1.5] [3.9 1.1] [4.8 1.8] [4. 1.3] [4.9 1.5] [4.7 1.2] [4.3 1.3] [4.4 1.4] [4.8 1.4] [5. 1.7] [4.5 1.5] [3.5 1. ] [3.8 1.1] [3.7 1. ] [3.9 1.2] [5.1 1.6] [4.5 1.5] [4.5 1.6] [4.7 1.5] [4.4 1.3] [4.1 1.3] [4. 1.3] [4.4 1.2] [4.6 1.4] [4. 1.2] [3.3 1. ] [4.2 1.3] [4.2 1.2] [4.2 1.3] [4.3 1.3] [3. 1.1] [4.1 1.3] [6. 2.5] [5.1 1.9] [5.9 2.1] [5.6 1.8] [5.8 2.2] [6.6 2.1] [4.5 1.7] [6.3 1.8] [5.8 1.8] [6.1 2.5] [5.1 2. ] [5.3 1.9] [5.5 2.1] [5. 2. ] [5.1 2.4] [5.3 2.3] [5.5 1.8] [6.7 2.2] [6.9 2.3] [5. 1.5] [5.7 2.3] [4.9 2. ] [6.7 2. ] [4.9 1.8] [5.7 2.1] [6. 1.8] [4.8 1.8] [4.9 1.8] [5.6 2.1] [5.8 1.6] [6.1 1.9] [6.4 2. ] [5.6 2.2] [5.1 1.5] [5.6 1.4] [6.1 2.3] [5.6 2.4] [5.5 1.8] [4.8 1.8] [5.4 2.1] [5.6 2.4] [5.1 2.3] [5.1 1.9] [5.9 2.3] [5.7 2.5] [5.2 2.3] [5. 1.9] [5.2 2. ] [5.4 2.3] [5.1 1.8]] x_train,x_test,y_train,y_test=model_selection.train_test_split(x,y,random_state=101,test_size=0.3) print("split_train_data 70%:", x_train.shape, "split_train_target 70%:",y_train.shape, "split_test_data 30%", x_test.shape, "split_test_target 30%",y_test.shape) split_train_data 70%: (105, 2) split_train_target 70%: (105,) split_test_data 30% (45, 2) split_test_target 30% (45,) # K-Nearest Neighbours model=KNeighborsClassifier(n_neighbors=3) model.fit(x_train, y_train) prediction=model.predict(x_test) print('The accuracy of the K-Nearest Neighbours is: {0}'.format(metrics.accuracy_score(prediction,y_test))) The accuracy of the K-Nearest Neighbours is: 0.9777777777777777

由此可见，同样选取了准确率很高的KNN模型，选取花萼特征和花瓣特征分别进行训练，得到的预测准确度是不同的。花萼特征相关度较低，花瓣特征相关度较高，花瓣训练结果较为乐观，而四个特征一起加入训练时准确率最高，可以达到100%.